There is growing evidence of genetic and neurobiological differences between readers with developmental dyslexia (DD) and typically developing (TD) readers, but there is no consensus on the specific origins of DD. The central goal of the proposed research is to develop a well specified brain-based account of DD, to better understand how neurocomputational mechanisms give rise to behavioral deficits, and their relation to response to treatment. Project 1 will use integrated cognitive and neuroimaging experiments to examine neurocomputational mechanisms that have been hypothesized to interfere with reading acquisition in DD resisters, by placing limits on processing speed, capacity, and learning and consolidation. Computational modeling will clarify the nature of these neurocomputational mechanisms and provide evidence as to which types and degrees of impairment(s) give rise to the behavioral profiles of different DD participant groups. Using younger (3rd/4th graders) and older (7th graders) cohorts of DD readers, it will develop detailed behavioral and neurobiological profiles of DDs who are treatment resisters and responders, to be analyzed with a focus on isolating specific factors that differentiate them, and predict variability within well-defined subgroups at different ages. A total of 120 3rd/4th graders, and 70 7th/8th graders, will receive treatment (Phase I). After post-testing, 30 resisters and 30 responders from each age category will be identified for the Phase II. In Phase II, in addition to the 60 DD responders and 60 DD resisters, 60 typically developing (TD) 3''*4* graders, and 60 TD 7'^'8''' grade controls, will participate in the cognitive and neuroimaging experiments. Phase II includes an fMRI session using the N-back task, structural scanning to measure grey matter volume, and DTI measures. Phase II also includes linguistic and non-linguistic learning and plasticity experiments, which are used to evaluate those brain-related predictors of group and individual differences in rate and retention. Ultimately, through this identification of the neurocomputation mechanisms that best describe DD resisters and responders at different ages, this work will inform development of better-tailored and optimized treatments for DD.